Covid Report for Family

Background

Notes and format last updated May 7, 2020

Starting on the May 7th update, the NY Times began including probable covid cases/deaths along with confirmed. This mostly affects death counts – for certain geographies that include probable COVID deaths in addition to confirmed, these are now added to the totals. For the time being, they were all added to the May 6th totals, causing a big spike at the U.S. level. Over time, NY Times will revise their historical counts and distribute these added deaths when they actually occurred, so the spike should fade.

• This page shows COVID data broken down into regions that affect our family. It pulls from publicly available raw data from the NY Times, which is reported daily. It’s on a one-day lag, so when you see this report, the most recent day included will be that reported the day prior.
• The table of contents above summarizes the major sections and can be used to jump to those sections.
• Each major section is then divided into tabs to allow you to more easily shuffle between geographies and metrics.
  • If you open on your phone, I don’t believe the tabs will work, so you’ll just see the page as one long, continuous document.
• More detailed descriptions and context can be found in each section.

Growth rates

Heat maps

• The two heat maps below compare how quickly total cases or deaths have grown at various times in our respective geopgraphies.
• The first plot compares growth rate for total cases; the second, growth rate for total deaths.
• The metric used is doubling time, by which I mean how quickly total cases or deaths are doubling.
• The plots track that doubling time at each date for our geographies. Darker colors reflect shorter doubling times, and thus periods of faster growth.
  • You can use the plots to track each geography over time and to compare the geographies to one another.
  • You can also compare the cases and death charts, to see how faster periods of death growth follow faster periods of case growth.

Case growth rates

• This section charts the growth rate of both total and new cases for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total cases and new cases.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of cases, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new cases) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new case reporting at the state level.
  • For total cases, the trendlines are a rolling 3-day average of daily growth rates in total cases. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total cases have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total case line get as close to zero as possible.
  • For new cases, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new cases. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total cases, we want to watch for the lines for new cases to get consistently below zero and stay there. That means that we are consistently seeing fewer new cases on a daily basis.

U.S.

Our states

Death growth rates

• This section charts the growth rate of both total and new deaths for each of our respective geographies. Each geography has its own chart, and then that chart will have a trendline for total deaths and new deaths.
  • There are only plots for the U.S. and states because the numbers for the counties are too small to generate worthwhile trendlines in this section.
• Note that we’re charting growth rate and not a count of deaths, so don’t think of these as the standard “curve” that we hear about in the news and that we want to flatten. Instead, these growth rate charts help track more precisely what we can only estimate when we see those other curves. For these growth rate charts, if the line is above zero, the metric we are tracking (total or new deaths) is continuing to grow. If the growth rate line is going up, it’s growing more quickly each day; if it’s going down but still above zero, it’s growing less quickly (but still growing). Only when the growth rate lines go below zero has the metric stopped growing.
• Each of these two lines uses rolling windows to calculate a growth rate for that particular metric. I do the calculation differently for each to smooth out some of the large day-to-day discrepancies in new death reporting at the state level.
  • For total deaths, the trendlines are a rolling 3-day average of daily growth rates in total deaths. We want to see these decline (and almost all are), but they can’t go below zero. This is because we’re tracking growth rate and a growth rate line below zero would mean total deaths have gone down, which can’t happen. They can only grow less quickly, which means we want to see the total death line get as close to zero as possible.
  • For new deaths, the trendlines show a rolling 3-day average of daily growth rate in the rolling 7-day average of new deaths. Including two rolling periods in this average helps smooth out crazy spikes at the state level that result from large day-to-day changes. Unlike the lines for total deaths, we want to watch for the lines for new deaths to get consistently below zero and stay there. That means that we are consistently seeing fewer new deaths on a daily basis.

U.S.

Our states

By population rankings

This section tracks metrics for states and counties normalized for population (number of cases or deaths per million residents), and then compares these figures both for our geographies and the country overall.

States

• This section shows tables ranking all 50 states for per populations rates of total cases, new cases, total deaths, and new deaths.
• For each metric, in addition to the tables, the trends for the top states are plotted over time.
  • We only plot the top ten states for each metric so that the plots aren’t too crowded. But you can view the full 50-state rankings in the tables.

Total confirmed cases

Table of total confirmed cases per million residents (all 50 states)

Ranking	State	Cases Per Million
1	Louisiana	29,815
2	Florida	26,822
3	Arizona	26,656
4	Mississippi	24,330
5	Alabama	22,231
6	New York	22,136
7	New Jersey	21,346
8	Georgia	20,894
9	South Carolina	20,772
10	Nevada	20,135
11	Texas	19,625
12	Rhode Island	19,419
13	Tennessee	19,291
14	District of Columbia	18,806
15	Massachusetts	17,967
16	Arkansas	17,587
17	Delaware	16,981
18	Maryland	16,751
19	Iowa	16,744
20	Illinois	16,567
21	California	16,070
22	Nebraska	15,799
23	Idaho	15,731
24	Utah	14,640
25	Connecticut	14,379
26	North Carolina	13,915
27	Virginia	12,585
28	Indiana	12,315
29	Oklahoma	12,310
30	Kansas	12,200
31	Wisconsin	12,159
32	South Dakota	11,710
33	Minnesota	11,660
34	Missouri	11,378
35	North Dakota	11,352
36	New Mexico	11,207
37	Michigan	10,300
38	Pennsylvania	10,130
39	Ohio	9,330
40	Kentucky	9,313
41	Colorado	9,271
42	Washington	9,259
43	Puerto Rico	8,379
44	Alaska	6,982
45	Wyoming	5,755
46	Oregon	5,562
47	Montana	5,436
48	New Hampshire	5,151
49	West Virginia	4,816
50	Hawaii	3,666
51	Maine	3,122
52	Vermont	2,447

New confirmed cases

Table of new cases per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Cases Per Million
1	Texas	274
2	Nevada	240
3	Tennessee	208
4	Georgia	204
5	Florida	198
6	Missouri	194
7	Alabama	183
8	California	183
9	Puerto Rico	170
10	Mississippi	165
11	Iowa	164
12	Hawaii	158
13	Oklahoma	153
14	Idaho	149
15	Kansas	148
16	North Dakota	142
17	South Carolina	136
18	Illinois	134
19	South Dakota	126
20	Louisiana	125
21	Kentucky	119
22	Indiana	118
23	Minnesota	117
24	Wisconsin	116
25	Alaska	109
26	North Carolina	107
27	Arizona	101
28	Maryland	101
29	Virginia	100
30	Nebraska	99
31	Utah	92
32	Wyoming	85
33	Washington	84
34	Montana	76
35	Arkansas	75
36	Rhode Island	74
37	District of Columbia	73
38	Ohio	71
39	Michigan	68
40	Delaware	67
41	Oregon	66
42	West Virginia	66
43	New Mexico	54
44	Massachusetts	53
45	Pennsylvania	49
46	Colorado	46
47	Connecticut	34
48	New York	30
49	New Jersey	22
50	Maine	20
51	Vermont	13
52	New Hampshire	9

Total deaths

Table of total deaths per million residents (all 50 states)

Ranking	State	Deaths Per Million
1	New Jersey	1,791
2	New York	1,667
3	Massachusetts	1,282
4	Connecticut	1,249
5	Louisiana	973
6	Rhode Island	965
7	District of Columbia	845
8	Mississippi	703
9	Michigan	660
10	Illinois	630
11	Arizona	619
12	Delaware	608
13	Maryland	602
14	Pennsylvania	588
15	Indiana	465
16	Florida	444
17	South Carolina	444
18	Georgia	435
19	Alabama	392
20	Texas	364
21	Nevada	349
22	New Mexico	342
23	Colorado	330
24	Ohio	327
25	Iowa	312
26	Minnesota	311
27	New Hampshire	311
28	California	286
29	Virginia	279
30	Washington	245
31	Missouri	239
32	North Carolina	227
33	Tennessee	200
34	Arkansas	199
35	Kentucky	188
36	Nebraska	187
37	Wisconsin	180
38	South Dakota	172
39	North Dakota	170
40	Oklahoma	168
41	Idaho	153
42	Kansas	140
43	Utah	114
44	Puerto Rico	104
45	Maine	94
46	Oregon	92
47	Vermont	92
48	West Virginia	89
49	Montana	76
50	Wyoming	57
51	Alaska	35
52	Hawaii	27

New deaths

Table of new deaths per million residents: rolling 3-day average (all 50 states)

Ranking	State	New Deaths Per Million
1	Florida	6
2	Louisiana	6
3	Mississippi	5
4	South Carolina	5
5	Georgia	4
6	Texas	4
7	Arizona	3
8	Nevada	3
9	Alabama	2
10	Missouri	2
11	New Mexico	2
12	North Dakota	2
13	Tennessee	2
14	Arkansas	1
15	California	1
16	District of Columbia	1
17	Idaho	1
18	Indiana	1
19	Iowa	1
20	Kentucky	1
21	Massachusetts	1
22	Minnesota	1
23	North Carolina	1
24	Ohio	1
25	Oklahoma	1
26	Puerto Rico	1
27	South Dakota	1
28	Washington	1
29	Wyoming	1
30	Alaska	0
31	Colorado	0
32	Connecticut	0
33	Delaware	0
34	Hawaii	0
35	Illinois	0
36	Kansas	0
37	Maine	0
38	Maryland	0
39	Michigan	0
40	Montana	0
41	Nebraska	0
42	New Hampshire	0
43	New Jersey	0
44	New York	0
45	Oregon	0
46	Pennsylvania	0
47	Rhode Island	0
48	Utah	0
49	Vermont	0
50	Virginia	0
51	West Virginia	0
52	Wisconsin	0

Counties

• This section focuses on the county level. It shows tables with our counties ranked by percentile of U.S. counties for per population rates of total cases and total deaths.
  • Each table also shows the top five counties in the country in addition to our counties, for added perspecive.
• In addition to the tables, our counties’ percentile for both total cases and total deaths are plotted over time.

Confirmed cases

Table showing total cases per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Cases Per Million	Raw Ranking	Percentile
Trousdale	Tennessee	140,642	1	99
Lafayette	Florida	119,805	2	99
Lake	Tennessee	114,595	3	99
Lee	Arkansas	110,534	4	99
Lincoln	Arkansas	103,501	5	99
Davidson	Tennessee	35,134	146	95
Richland	South Carolina	22,619	407	87
Orange	California	13,832	913	70
York	South Carolina	13,549	948	69
Pierce	Washington	7,487	1690	46

Our county percentiles over time

Deaths

Table showing total deaths per million and percentile for all US counties. Includes our counties and the top 5 in the US for perspective.

County	State	Deaths Per Million	Raw Ranking	Percentile
Galax city	Virginia	4,254	1	99
Hancock	Georgia	4,139	2	99
Randolph	Georgia	3,836	3	99
Terrell	Georgia	3,517	4	99
Neshoba	Mississippi	3,297	5	99
Richland	South Carolina	409	672	78
Davidson	Tennessee	331	827	73
Orange	California	255	1032	67
Pierce	Washington	173	1331	57
York	South Carolina	114	1639	47

Our county percentiles over time

Raw counts

• This section shows the raw counts over time for the U.S., our states, and our counties for four categories:
  • Total confirmed cases: This is the cumulative total of cases reported.
  • New confirmed cases: This is the number of new cases reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new cases, to help better visualize the peak.
  • Total deaths: This is the cumulative total of deaths reported.
  • New deaths: This is the number of new deaths reported on each respective date.
    • For the U.S. and states, a trendline shows the rolling 7-day average of new deaths, to help better visualize the peak.

Total confirmed cases

U.S.

Our states

Our counties

New confirmed cases

U.S.

Our states

Our counties

Total deaths

U.S.

Our states

Our counties

New deaths

U.S.

Our states

Our counties

Stay-at-home comparisons

• This section is a work in progress. It’s exploring what effect stay-at-home orders have on spread. It’s inherentely messy because some states have localized orders but not orders for their full state and because states implemented the orders at different times.
  • The first plot shows a rolling 3-day average of new cases per million residents, averaged across 2 groups of states – those with a state-wide stay-at-home order in effect and those without. The size of the dots reflect the number of states in each group for each given day (so the dots are getting bigger over time for the group with orders, as more states implement those orders).
    • So far, this view doesn’t show if these orders are “working” or not. Instead, the biggest takeaway from the current plot is that the states that implemented orders are reacting to having worse spread, which is why the case rate is so much higher at the start for them. As a state decides it has a problem and implements an order, it joins the grouping of states with orders and drives the rate up for that group.
    • Over time (if the policies work), what we’d expect is that the line for the group with orders flattens or declines in relation to the no-order group. It also may become moot if all states issue orders.
    • As states start rescending their orders, I’ll put them back into the no-order group, so the no-order group should beging to expand again.
  • The second plot tracks progress of states by the number of days since orders have been in effect. It includes only those states with orders and shows their average new case growth rate (a rolling 3-day average of daily growth rates) based on how long it’s been since the order went into effect. Again, the size of the dots show how many states are included in each point – i.e., as you go further right, the dots are smaller because less states are that far past their order.
    • I’m working on how to add a baseline to this chart to compare to states without orders, but that’s challenging because there’s no way to fully align timing.